class_docstr: Main class for data parallel.
  
  This class supports GCQ (Gradient Communication Quantization) for
  distributed multi-machine training based on fairseq.trainer.Trainer.
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
file_path: lightseq/lightseq/training/gcq/ls_fs_gcq_trainer.py
imports: import logging
  import torch
  import torch.distributed
  from torch.nn.parallel import DistributedDataParallel
  from fairseq.trainer import Trainer
  from packaging import version
  from .gcq import GCQState, encode_and_decode
label_desc: Use image node_id 1 to create a new LSTrainer object from inherited base classes: Trainer with example: obj = LSTrainer()